[gkamradt]

Hey HN, Greg from ARC Prize Foundation here.

Alongside Mike Knoop and François Francois Chollet, we’re launching ARC-AGI-2, a frontier AI benchmark that measures a model’s ability to generalize on tasks it hasn’t seen before, and the ARC Prize 2025 competition to beat it.

In Dec ‘24, ARC-AGI-1 (2019) pinpointed the moment AI moved beyond pure memorization as seen by OpenAI's o3.

ARC-AGI-2 targets test-time reasoning.

My view is that good AI benchmarks don't just measure progress, they inspire it. Our mission is to guide research towards general systems.

Base LLMs (no reasoning) are currently scoring 0% on ARC-AGI-2. Specialized AI reasoning systems (like R1 or o3-mini) are <4%.

Every (100%) of ARC-AGI-2 tasks, however, have been solved by at least two humans, quickly and easily. We know this because we tested 400 people live.

Our belief is that once we can no longer come up with quantifiable problems that are "feasible for humans and hard for AI" then we effectively have AGI. ARC-AGI-2 proves that we do not have AGI.

Change log from ARC-AGI-2 to ARC-AGI-2: * The two main evaluation sets (semi-private, private eval) have increased to 120 tasks * Solving tasks requires more reasoning vs pure intuition * Each task has been confirmed to have been solved by at least 2 people (many more) out of an average of 7 test taskers in 2 attempts or less * Non-training task sets are now difficulty-calibrated

The 2025 Prize ($1M, open-source required) is designed to drive progress on this specific gap. Last year's competition (also launched on HN) had 1.5K teams participate and had 40+ research papers published.

The Kaggle competition goes live later this week and you can sign up here: https://arcprize.org/competition

We're in an idea-constrained environment. The next AGI breakthrough might come from you, not a giant lab.

Happy to answer questions.

[Chathamization]

> Our belief is that once we can no longer come up with quantifiable problems that are "feasible for humans and hard for AI" then we effectively have AGI.

I don’t think that follows. Just because people fail to create ARC-AGI problems that are difficult for an AI to solve, doesn’t mean that said AI can just be plugged into a humanoid robot and it will now reliably cook dinner, order a pizza and drive to pick it up, take a bus to downtown to busk on the street and take the money back home, etc.

ARC-AGI is an interesting benchmark, but it’s extremely presumptive to think that these types of tests are going to demonstrate AGI.

[Palmik]

In your example you already indicated two tasks that you think might be hard for AI but easy for humans.

Who said that cooking dinner couldn't be part of ARC-AGI-<N>?

[jononor]

The task you mention require intelligence but also a robot body with a lot of physical dexterity suited to a designed-for-humanoids world. That seems like an additional requirement on top of intelligence? Maybe we do not want an AGI definition to include that?

There are humans who cannot perform these tasks, at least without assistive/adapted systems such as a wheelchair and accessible bus.

[CooCooCaCha]

The statement you quoted is a general statement, not specific to ARC-AGI.

The scenarios you listed are examples of what they’re talking about. Those are tasks that humans can easily do but robots have a hard time with.

[yorwba]

The point isn't demonstrating AGI, but rather demonstrating that AGI definitely hasn't been reached yet.

[artninja1988]

What are you doing to prevent the test set being leaked? Will you still be offering API access to the semi private test set to the big model providers who presumably train on their API?

[gkamradt]

We have a few sets:

1. Public Train - 1,000 tasks that are public 2. Public Eval - 120 tasks that are public

So for those two we don't have protections.

3. Semi Private Eval - 120 tasks that are exposed to 3rd parties. We sign data agreements where we can, but we understand this is exposed and not 100% secure. It's a risk we are open to in order to keep testing velocity. In theory it is very difficulty to secure this 100%. The cost to create a new semi-private test set is lower than the effort needed to secure it 100%.

4. Private Eval - Only on Kaggle, not exposed to any 3rd parties at all. Very few people have access to this. Our trust vectors are with Kaggle and the internal team only.

[Centigonal]

Thank you for including cost (or really any proxy for efficiency) as a dimension to this prize!

[levocardia]

I'm really pleased to see this! The original ARC-AGI-1 paper still informs how I think about "what is intelligence" today. I was thrilled to see AI models make real progress on that test precisely when we had the next big idea (reasoning). Here's to hoping round 2 falls with a similarly big breakthrough!

[az226]

Did any single individual solve all problems? How many such individuals were there?

[tananaev]

Did I read this right that only 2 humans out of 400 solved the problems?

[trott]

They started with N >= 120x3 tasks, and gave each task to 4-9 humans. Then they kept only those 120x3 tasks that at least 2 humans had solved.

[mapmeld]

No, they're saying that the problems have been reviewed / play-tested by ≥2 humans, so they are not considered unfair or too ambiguous to solve in two attempts (a critique of some Arc-AGI-1 puzzles that o3 missed). They have a lot of puzzles so they were divided among some number of testers, but I don't think every tester had to try every problem.

[gmkhf]

I think a lot of people got discouraged, seeing how openai solved arc agi 1 by what seems like brute forcing and throwing money at it. Do you believe arc was solved in the "spirit" of the challenge? Also all the open sourced solutions seem super specific to solving arc. Is this really leading us to human level AI at open ended tasks?

[jmtulloss]

Why is this the same comment as https://news.ycombinator.com/item?id=43466406?

[vessenes]

Just want to say I really love these new problems - feels like some general intelligence went into conceiving of and creating these puzzles: we just did a few over dinner as a family.

You have my wheels turning on how to get computers better at these. Looking forward to see G the first computer tech that can get 30-50% on these!

[Nuzzerino]

Why wasn’t the ICOM framework (D. Kelley) allowed to make a scoring submission after they claimed to have beaten the scores? Are you concerned that may appear to contradict your mission statement and alienate the AGI community?

[az226]

Which puzzles had the lowest solve rate? I did the first 10 and felt all easy (mentally solve it in 10-20 seconds for easier ones and 30-60 seconds for harder ones), I’d like to try the most difficult ones.

[synapsomorphy]

Thanks for your awesome work Greg!

The success of o3 directly contradicts us being in an "idea-constrained environment", what makes you believe that?

[littlestymaar]

What makes you think so?

From ChatGPT 3.5 to o1, all LLMs progress came from investment in training: either by using much more data, or using higher quality data thanks to artificial data.

o1 (and then o3) broke this paradigm by applying a novel idea (RL+search on CoT) and that's because of it that it was able to make progress on ARC-AGI.

So IMO the success of o3 goes in favor of the argument of how we are in an idea-constrained environment.

[jononor]

Not Greg/team, so unrelated opinion. o3 solution for ARC v1 was incredibly expensive. Some good ideas are at least needed to take that cost down by a factor 100-10000x.

[ustad]

Using AGI in the titles of your tests might not be accurate or appropriate. May I suggest NAI - Narrow AI?

[JFingleton]

My prediction: we'll be arguing about what AGI actually is... Forever.

[doctorpangloss]

Why doesn’t every blogpost contain an example of a question you ask?